基于改进YOLOv8的无人机图像玉米幼苗检测

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中图分类号：S513 文献标识码：A 文章编号：1000-4440（2025）06-1179-09

Abstract：Unmannedaerial vehicletechnology，withits higheficiencyandprecision，hasbeen playingan importantrole intheagricultural fieldandhasben widelyappedinfarmlandmonitoring，precisionfertilization，andpestanddiseasecontrol. However，inthecomsedingimagescolectedbyunmannederialvehicle，weedsndotherinterferentsinthefieldhavecolors similar tocomseedlings，whichcaneasilylead tofalsedetectionand mised detectionintheYOLOv8 model.Additionally，the occlusionbetweencornsedlingscanalsoafectthedetectionaccuracyof the model.Inresponsetotheseproblems，this study proposed the YOLOv8+MultiSEAM + MetaNeXtStag + WIoU（YOLOv8-MMW）model. Based on the YOLOv8 model architecture， this modelfirstintroducedthe MultiSEAMatentionmechanismintothenecknetwork，whichefectivelyenhancedthemodel's featureextractionabilityincomplexscenes.SecondlyitincorporatedtheMetaNeXtStagemodulefromInceptionNeXt.Onhisbasis，theWise-IoUlossfunctionwasadoptedtoimprovemodelaccuracy.Theexperimentalresultsonthetestsetshowedthatthe accuracy and the mean average precision at an intersection-over-union threshold of O.50（ mAP50 ）ofthe YOLOv8-MMW model

reach 98.9% and 89.6% ，respectively，which were 6.1 percentage points and 2.4 percentage points higher than those ofthe original YOLOv8n model.The YOLOv8-MMW model proposed in this study demonstrates stronger robustnessin complex farmland environmentsand can effectively improve the detection accuracy of corn seedling images captured by inmanned aerial vehicle，and can provide technical support for agricultural management and monitoring

Key words：corn seedling；unmanned aerial vehicle；YOLOv8；MultiSEAM；MetaNeXtStage；lossfunctior

玉米是中国种植面积1和产量均居首位的作物2，也是重要的经济作物，对国家粮食安全具有重大战略意义[3]。（剩余12959字）